Honda Used Car Selling Analysis¶

In [1]:
import pandas as pd
import numpy as np
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objs as go
In [2]:
#Reading csv file and converting as DataFrame
df=pd.read_csv("C:/Users/shakt/Downloads/honda_car_selling.csv")
df
Out[2]:
Year kms Driven Fuel Type Suspension Price Car Model
0 2019 19006 kms Petrol Automatic 9.29 Lakh Honda City V CVT
1 2021 11936 kms Petrol Automatic 13.95 Lakh Honda City ZX CVT
2 2018 29635 kms Petrol Automatic 9.95 Lakh Honda City i-VTEC CVT ZX
3 2020 16163 kms Petrol Automatic 13.26 Lakh Honda City ZX CVT
4 2015 105114 kms Petrol Manual 5.68 Lakh Honda City i VTEC V
... ... ... ... ... ... ...
994 2007 90000 kms Petrol Manual 1.22 Lakh Honda Civic 1.8 V MT
995 2016 31500 kms Petrol Manual 4.25 Lakh Honda Amaze S i-VTEC
996 2017 39735 kms Petrol Manual 5.10 Lakh Honda Amaze S Petrol
997 2017 36000 kms Petrol Manual 6.10 Lakh Honda City i VTEC S
998 2015 35341 kms Diesel Manual 3 Lakh Honda Amaze S i-Dtech

999 rows × 6 columns

In [3]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 999 entries, 0 to 998
Data columns (total 6 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   Year        999 non-null    int64 
 1   kms Driven  999 non-null    object
 2   Fuel Type   999 non-null    object
 3   Suspension  999 non-null    object
 4   Price       999 non-null    object
 5   Car Model   999 non-null    object
dtypes: int64(1), object(5)
memory usage: 47.0+ KB
In [4]:
#removing char part from kms Driven column value and convert it as int type
df['kms Driven']=df['kms Driven'].str.replace('kms','').astype(int)
In [5]:
df
Out[5]:
Year kms Driven Fuel Type Suspension Price Car Model
0 2019 19006 Petrol Automatic 9.29 Lakh Honda City V CVT
1 2021 11936 Petrol Automatic 13.95 Lakh Honda City ZX CVT
2 2018 29635 Petrol Automatic 9.95 Lakh Honda City i-VTEC CVT ZX
3 2020 16163 Petrol Automatic 13.26 Lakh Honda City ZX CVT
4 2015 105114 Petrol Manual 5.68 Lakh Honda City i VTEC V
... ... ... ... ... ... ...
994 2007 90000 Petrol Manual 1.22 Lakh Honda Civic 1.8 V MT
995 2016 31500 Petrol Manual 4.25 Lakh Honda Amaze S i-VTEC
996 2017 39735 Petrol Manual 5.10 Lakh Honda Amaze S Petrol
997 2017 36000 Petrol Manual 6.10 Lakh Honda City i VTEC S
998 2015 35341 Diesel Manual 3 Lakh Honda Amaze S i-Dtech

999 rows × 6 columns

In [6]:
#finding the no of duplicated rows of the df
df.duplicated().value_counts()
Out[6]:
False    974
True      25
dtype: int64
In [7]:
#viewing df which has all the duplicated rows (all occurances)
df.loc[df.duplicated(keep=False)]
Out[7]:
Year kms Driven Fuel Type Suspension Price Car Model
221 2018 43326 Petrol Manual 4.85 Lakh Honda Amaze E i-VTEC
223 2018 43326 Petrol Manual 4.85 Lakh Honda Amaze E i-VTEC
307 2017 51000 Petrol Automatic 8.99 Lakh Honda City i-VTEC CVT ZX
326 2012 48000 Petrol Manual 2.75 Lakh Honda Brio S MT
419 2020 60714 Petrol Manual 6.90 Lakh Honda Amaze S Petrol
515 2017 51000 Petrol Automatic 8.99 Lakh Honda City i-VTEC CVT ZX
734 2021 38352 Petrol Manual 7.85 Lakh Honda Amaze VX Petrol
735 2021 38352 Petrol Manual 7.85 Lakh Honda Amaze VX Petrol
739 2020 60714 Petrol Manual 6.90 Lakh Honda Amaze S Petrol
786 2012 48000 Petrol Manual 2.75 Lakh Honda Brio S MT
812 2008 80000 Petrol Manual 2 Lakh Honda Civic 1.8 V MT
960 2016 28000 Petrol Manual 4.70 Lakh Honda Brio 1.2 VX MT
961 2017 30000 Petrol Manual 4.50 Lakh Honda Amaze S i-VTEC
962 2008 80000 Petrol Manual 2 Lakh Honda Civic 1.8 V MT
963 2016 35000 Diesel Manual 6.20 Lakh Honda Jazz 1.5 VX i DTEC
964 2008 110000 Petrol Manual 1.25 Lakh Honda Civic 1.8 V MT
965 2015 37326 Petrol Manual 5 Lakh Honda Jazz 1.2 S i VTEC
966 2018 60000 Diesel Manual 9 Lakh Honda City i-DTEC V
967 2007 110000 Petrol Manual 4.50 Lakh Honda CR-V RVi MT
968 2016 50000 Petrol Automatic 7.80 Lakh Honda City i VTEC CVT SV
969 2006 110000 Petrol Manual 1.80 Lakh Honda City GXi
970 2017 16665 Petrol Automatic 11.50 Lakh Honda City i-VTEC CVT VX
971 2013 110000 Diesel Manual 3.35 Lakh Honda Amaze EX i-Dtech
972 2019 47297 Petrol Automatic 15 Lakh Honda Civic ZX
973 2006 186719 Petrol Manual 90,000 Honda City GXi
974 2007 90000 Petrol Manual 1.22 Lakh Honda Civic 1.8 V MT
975 2016 31500 Petrol Manual 4.25 Lakh Honda Amaze S i-VTEC
976 2017 39735 Petrol Manual 5.10 Lakh Honda Amaze S Petrol
977 2017 36000 Petrol Manual 6.10 Lakh Honda City i VTEC S
978 2015 35341 Diesel Manual 3 Lakh Honda Amaze S i-Dtech
980 2016 28000 Petrol Manual 4.70 Lakh Honda Brio 1.2 VX MT
981 2017 30000 Petrol Manual 4.50 Lakh Honda Amaze S i-VTEC
982 2008 80000 Petrol Manual 2 Lakh Honda Civic 1.8 V MT
983 2016 35000 Diesel Manual 6.20 Lakh Honda Jazz 1.5 VX i DTEC
984 2008 110000 Petrol Manual 1.25 Lakh Honda Civic 1.8 V MT
985 2015 37326 Petrol Manual 5 Lakh Honda Jazz 1.2 S i VTEC
986 2018 60000 Diesel Manual 9 Lakh Honda City i-DTEC V
987 2007 110000 Petrol Manual 4.50 Lakh Honda CR-V RVi MT
988 2016 50000 Petrol Automatic 7.80 Lakh Honda City i VTEC CVT SV
989 2006 110000 Petrol Manual 1.80 Lakh Honda City GXi
990 2017 16665 Petrol Automatic 11.50 Lakh Honda City i-VTEC CVT VX
991 2013 110000 Diesel Manual 3.35 Lakh Honda Amaze EX i-Dtech
992 2019 47297 Petrol Automatic 15 Lakh Honda Civic ZX
993 2006 186719 Petrol Manual 90,000 Honda City GXi
994 2007 90000 Petrol Manual 1.22 Lakh Honda Civic 1.8 V MT
995 2016 31500 Petrol Manual 4.25 Lakh Honda Amaze S i-VTEC
996 2017 39735 Petrol Manual 5.10 Lakh Honda Amaze S Petrol
997 2017 36000 Petrol Manual 6.10 Lakh Honda City i VTEC S
998 2015 35341 Diesel Manual 3 Lakh Honda Amaze S i-Dtech
In [8]:
#removing the duplicates keeping the first occurences as orig
df=df.drop_duplicates(keep='first')
In [9]:
df
Out[9]:
Year kms Driven Fuel Type Suspension Price Car Model
0 2019 19006 Petrol Automatic 9.29 Lakh Honda City V CVT
1 2021 11936 Petrol Automatic 13.95 Lakh Honda City ZX CVT
2 2018 29635 Petrol Automatic 9.95 Lakh Honda City i-VTEC CVT ZX
3 2020 16163 Petrol Automatic 13.26 Lakh Honda City ZX CVT
4 2015 105114 Petrol Manual 5.68 Lakh Honda City i VTEC V
... ... ... ... ... ... ...
975 2016 31500 Petrol Manual 4.25 Lakh Honda Amaze S i-VTEC
976 2017 39735 Petrol Manual 5.10 Lakh Honda Amaze S Petrol
977 2017 36000 Petrol Manual 6.10 Lakh Honda City i VTEC S
978 2015 35341 Diesel Manual 3 Lakh Honda Amaze S i-Dtech
979 2017 8602 Petrol Manual 7.25 Lakh Honda City V MT AVN

974 rows × 6 columns

In [10]:
# Checking if any null value is there in any column
df.isna().sum()
Out[10]:
Year          0
kms Driven    0
Fuel Type     0
Suspension    0
Price         0
Car Model     0
dtype: int64
In [11]:
#To see top10 most common car models with year have come for the sale 
df_year_car_model=df.groupby(['Year', 'Car Model']).size().nlargest(10).to_frame().reset_index()
df_year_car_model.columns=['Year','Car Model', 'Count']
df_year_car_model
Out[11]:
Year Car Model Count
0 2009 Honda City 1.5 S MT 21
1 2010 Honda City 1.5 S MT 19
2 2014 Honda Amaze S i-Dtech 16
3 2013 Honda Brio S MT 13
4 2014 Honda City i DTEC SV 13
5 2017 Honda City i VTEC V 13
6 2015 Honda Amaze S i-Vtech 11
7 2015 Honda City i VTEC V 11
8 2008 Honda City GXi 10
9 2014 Honda City i DTEC VX 10
In [12]:
#Graphical visualization
fig=px.bar(df_year_car_model,x='Year',y='Count',hover_name='Car Model',color='Car Model')
fig.show()
In [13]:
#To see how many number of each car brandas with three different types of fuel
df_fuel=df.groupby(['Fuel Type','Car Model']).size().to_frame().reset_index()
df_fuel.columns=['Fuel Type','Car Model','Count']
df_fuel
Out[13]:
Fuel Type Car Model Count
0 CNG Honda Amaze S Plus i-VTEC 1
1 Diesel Honda Amaze E Option i-DT 2
2 Diesel Honda Amaze E i-DTEC 1
3 Diesel Honda Amaze E i-Dtech 3
4 Diesel Honda Amaze EX i-Dtech 4
... ... ... ...
179 Petrol Honda WR-V Edge Edition i 2
180 Petrol Honda WR-V SV 1
181 Petrol Honda WR-V VX 2
182 Petrol Honda WR-V i-VTEC S 4
183 Petrol Honda WR-V i-VTEC VX 14

184 rows × 3 columns

In [14]:
#Graphical visual
fig=
fig=px.bar(df_fuel,x='Fuel Type',y='Count',                  
           hover_name='Car Model',
          color='Car Model',
          range_y=[0,1000])
          
fig.show()

  File "C:\Users\shakt\AppData\Local\Temp\ipykernel_31892\376909864.py", line 2
    fig=
        ^
SyntaxError: invalid syntax
In [ ]:
df

Petrol Car Analysis¶

In [ ]:
#To see the Car models under two different types of Suspension with fule type as Petrol
df_petrol = df[df['Fuel Type'] == ' Petrol ']
df_petrol_sus =df_petrol.groupby(['Suspension','Car Model']).size().to_frame().reset_index()
df_petrol_sus.columns=['Suspension','Car Model','Count']
df_petrol_sus
In [ ]:
#visualization tells that under each suspension what types of car models and how many number of cars with each model
fig=px.bar(df_petrol_sus,x='Suspension',y='Count',hover_name='Car Model',color='Car Model')
fig.show()
In [ ]:
#Finding the top 10 vehicles with the most miles driven when using petrol and Manual suspension
df_petrol_4year=df_petrol.loc[df_petrol['Year'].isin([2023,2022,2021,2020])]
df_petrol_4year_s=df_petrol_4year[df_petrol_4year['Suspension']==' Manual']
df_petrol_4year_s_top10_km=df_petrol_4year_s.sort_values('kms Driven',ascending=False).head(10)
df_petrol_4year_s_top10_km # top10 cars ran for highest km from the year b/w 2020 to 2023 with suspension type as Manual
In [ ]:
#Graphical representation
fig=go.Figure(go.Scatter(x=df_petrol_4year_s_top10_km['Car Model'],y=df_petrol_4year_s_top10_km['kms Driven'],mode='text+markers+lines',text=df_petrol_4year_s_top10_km['kms Driven']))
fig.update_layout( xaxis_title='Car Model', yaxis_title='kms Driven')
fig.show()
In [ ]:
#Finding the top 10 vehicles with the most miles driven when using petrol and automatic suspension
df_petrol_4year_s_a=df_petrol_4year[df_petrol_4year['Suspension']==' Automatic']
df_petrol_4year_s_a_top10_km=df_petrol_4year_s_a.sort_values('kms Driven',ascending=False).head(10)
df_petrol_4year_s_a_top10_km # top10 cars ran for highest km from the year b/w 2020 to 2023 with suspension type as Automatic
In [ ]:
#Graphical representation
fig=go.Figure(go.Scatter(x=df_petrol_4year_s_a_top10_km['Car Model'],y=df_petrol_4year_s_a_top10_km['kms Driven'],mode='text+markers+lines',text=df_petrol_4year_s_a_top10_km['kms Driven']))
fig.update_layout( xaxis_title='Car Model', yaxis_title='kms Driven')
fig.show()

Diesel Car Analysis¶

In [ ]:
df_diesel = df[df['Fuel Type'] == ' Diesel ']
df_diesel_sus =df_diesel.groupby(['Suspension','Car Model']).size().to_frame().reset_index()
df_diesel_sus.columns=['Suspension','Car Model','Count']
df_diesel_sus.head(10)
In [ ]:
#The image explains which car models are under each suspension as well as how many of each type there are, all using deisel fuel.
fig=px.bar(df_diesel_sus,x='Suspension',y='Count',hover_name='Car Model',color='Car Model')
fig.show()
In [ ]:
# filtering df with fuel type: Diesel, Suspension: Manual
df_desiel_4year=df_diesel.loc[df_diesel['Year'].isin([2023,2022,2021,2020])]
df_desiel_4year_s=df_desiel_4year[df_desiel_4year['Suspension']==' Manual']
df_desiel_4year_s_top_km=df_desiel_4year_s.sort_values('kms Driven',ascending=False)
df_desiel_4year_s_top_km
In [ ]:
# filstering df with fuel type: Diesel, Suspension: Manual
df_desiel_4year=df_diesel.loc[df_diesel['Year'].isin([2023,2022,2021,2020])]
df_desiel_4year_s=df_desiel_4year[df_desiel_4year['Suspension']==' Automatic']
df_desiel_4year_s_top_km=df_desiel_4year_s.sort_values('kms Driven',ascending=False)
df_desiel_4year_s_top_km

Price Range Analysis¶

In [15]:
#converting the price column with numerical values with lambda apply method
df['Price'] = df['Price'].apply(lambda x: float(x.replace('Lakh', ''))*100000 if x.endswith('Lakh') else float(x.replace(',', '')))

C:\Users\shakt\AppData\Local\Temp\ipykernel_31892\3333681110.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [16]:
#Finding top10 costliest cars 
df_top10_price=df.sort_values(by='Price',ascending=False).head(10)
df_top10_price
Out[16]:
Year kms Driven Fuel Type Suspension Price Car Model
390 2019 50175 Diesel Automatic 2650000.0 Honda CR-V Diesel 2WD
189 2019 33000 Petrol Automatic 2490000.0 Honda CR-V 2.0 CVT
394 2019 10745 Petrol Automatic 1754000.0 Honda Civic VX
469 2019 18000 Petrol Automatic 1725000.0 Honda Civic VX BSIV
858 2019 79000 Diesel Manual 1720000.0 Honda Civic VX Diesel
222 2022 1661 Petrol Manual 1600000.0 Honda City VX MT
398 2022 5100 Petrol Automatic 1590000.0 Honda City i-VTEC CVT ZX
15 2022 5500 Petrol Automatic 1590000.0 Honda City ZX CVT
400 2022 3800 Petrol Automatic 1590000.0 Honda City i-VTEC CVT ZX
36 2021 5282 Petrol Automatic 1526000.0 Honda City ZX CVT
In [48]:
#Graphical representation
fig=px.bar(df_top10_price,x='Car Model',y='Price',hover_name='Year',color='Car Model',text_auto=True)
fig.update_layout(width=800,height=400)
fig.show()
In [18]:
#Finding top10 Cheapest cars 
df_top10_price_Cheap=df.sort_values(by='Price',ascending=False).tail(10)
df_top10_price_Cheap
Out[18]:
Year kms Driven Fuel Type Suspension Price Car Model
652 2008 114502 Petrol Manual 100000.0 Honda Civic 1.8 V MT
764 2009 87163 Petrol Manual 100000.0 Honda City 1.5 S MT
572 2008 223000 Petrol Manual 100000.0 Honda City GXi
637 2009 78216 Petrol Manual 100000.0 Honda Jazz Basic
939 2008 92000 Petrol Manual 95000.0 Honda City GXi
973 2006 186719 Petrol Manual 90000.0 Honda City GXi
802 2007 120000 Petrol Manual 85000.0 Honda City VTEC
594 2008 39460 Petrol Manual 80000.0 Honda Civic 1.8 S MT
828 2005 110000 Petrol Manual 60000.0 Honda City 1.5 GXI
588 2003 149714 Petrol Manual 45000.0 Honda City 1.5 GXI
In [47]:
#Graphical visualization
fig=px.bar(df_top10_price_Cheap,x='Car Model',y='Price',hover_name='Year',color='Car Model',text_auto=True)
fig.update_layout(width=800,height=400)
fig.show()
In [20]:
#top10 costliest cars with fuel type as petrol and suspension type as Automatic
df_petrol_aut=df[(df['Fuel Type']==' Petrol ') & (df['Suspension']==' Automatic')]
df_petrol_aut_top10=df_petrol_aut.sort_values('Price',ascending=False).head(10)
df_petrol_aut_top10
Out[20]:
Year kms Driven Fuel Type Suspension Price Car Model
189 2019 33000 Petrol Automatic 2490000.0 Honda CR-V 2.0 CVT
394 2019 10745 Petrol Automatic 1754000.0 Honda Civic VX
469 2019 18000 Petrol Automatic 1725000.0 Honda Civic VX BSIV
15 2022 5500 Petrol Automatic 1590000.0 Honda City ZX CVT
398 2022 5100 Petrol Automatic 1590000.0 Honda City i-VTEC CVT ZX
400 2022 3800 Petrol Automatic 1590000.0 Honda City i-VTEC CVT ZX
36 2021 5282 Petrol Automatic 1526000.0 Honda City ZX CVT
972 2019 47297 Petrol Automatic 1500000.0 Honda Civic ZX
897 2019 35000 Petrol Automatic 1500000.0 Honda Civic VX BSIV
694 2020 5512 Petrol Automatic 1445000.0 Honda City ZX CVT
In [49]:
fig=px.histogram(df_petrol_aut_top10,x='Car Model',nbins=10,color='Price',text_auto=True)
fig.update_layout(width=800, height=500)
fig.show()
In [22]:
#top10 Cheapest cars with fuel typ as petrol and suspension type as Automatic
df_petrol_aut=df[(df['Fuel Type']==' Petrol ') & (df['Suspension']==' Automatic')]
df_petrol_aut_top10_ch=df_petrol_aut.sort_values('Price',ascending=False).tail(10)
df_petrol_aut_top10_ch
Out[22]:
Year kms Driven Fuel Type Suspension Price Car Model
851 2009 67300 Petrol Automatic 235000.0 Honda City 1.5 S AT
791 2010 90000 Petrol Automatic 230000.0 Honda City V AT
770 2007 130000 Petrol Automatic 200000.0 Honda Accord VTi-L (AT)
577 2008 100000 Petrol Automatic 200000.0 Honda Civic 1.8 S AT
482 2009 92034 Petrol Automatic 200000.0 Honda Civic 1.8 V AT
102 2008 120054 Petrol Automatic 196000.0 Honda Civic 1.8 V AT
532 2009 72000 Petrol Automatic 195000.0 Honda Civic 1.8 V AT
360 2008 71000 Petrol Automatic 195000.0 Honda Civic 1.8 V AT
506 2009 102816 Petrol Automatic 180000.0 Honda City 1.5 S AT
383 2009 107124 Petrol Automatic 175000.0 Honda City 1.5 V AT
In [50]:
#visualization
fig=px.histogram(df_petrol_aut_top10_ch,x='Car Model',nbins=10,color='Price',text_auto=True)
fig.update_layout(width=800, height=500)
fig.show()
In [24]:
#top10 costliest cars with fuel type as petrol and suspension type as Manual
df_petrol_man=df[(df['Fuel Type']==' Petrol ') & (df['Suspension']==' Manual')]
df_petrol_man_top10=df_petrol_man.sort_values('Price',ascending=False).head(10)
df_petrol_man_top10
Out[24]:
Year kms Driven Fuel Type Suspension Price Car Model
222 2022 1661 Petrol Manual 1600000.0 Honda City VX MT
397 2022 5045 Petrol Manual 1450000.0 Honda City ZX MT
95 2022 4696 Petrol Manual 1448000.0 Honda City ZX MT
854 2022 8700 Petrol Manual 1400000.0 Honda City ZX MT
451 2022 16000 Petrol Manual 1400000.0 Honda City i-VTEC ZX
695 2022 15500 Petrol Manual 1380000.0 Honda City ZX MT
736 2020 12487 Petrol Manual 1345000.0 Honda City i-VTEC S
16 2021 1425 Petrol Manual 1325000.0 Honda City VX MT
48 2020 20942 Petrol Manual 1296000.0 Honda City ZX MT
797 2020 35000 Petrol Manual 1295000.0 Honda City ZX MT
In [51]:
fig=px.histogram(df_petrol_man_top10,x='Car Model',nbins=10,color='Price',text_auto=True)
fig.update_layout(width=800, height=500)
fig.show()
In [26]:
#top10 cheapest cars with fuel type as petrol and suspension type as Manual
df_petrol_man=df[(df['Fuel Type']==' Petrol ') & (df['Suspension']==' Manual')]
df_petrol_man_top10_ch=df_petrol_man.sort_values('Price',ascending=False).tail(10)
df_petrol_man_top10_ch
Out[26]:
Year kms Driven Fuel Type Suspension Price Car Model
764 2009 87163 Petrol Manual 100000.0 Honda City 1.5 S MT
652 2008 114502 Petrol Manual 100000.0 Honda Civic 1.8 V MT
572 2008 223000 Petrol Manual 100000.0 Honda City GXi
637 2009 78216 Petrol Manual 100000.0 Honda Jazz Basic
939 2008 92000 Petrol Manual 95000.0 Honda City GXi
973 2006 186719 Petrol Manual 90000.0 Honda City GXi
802 2007 120000 Petrol Manual 85000.0 Honda City VTEC
594 2008 39460 Petrol Manual 80000.0 Honda Civic 1.8 S MT
828 2005 110000 Petrol Manual 60000.0 Honda City 1.5 GXI
588 2003 149714 Petrol Manual 45000.0 Honda City 1.5 GXI
In [52]:
fig=px.histogram(df_petrol_man_top10_ch,x='Car Model',nbins=10,color='Price',text_auto=True)
fig.update_layout(width=800, height=500)
fig.show()
In [28]:
#To see the df which has fuel type as Diesel and Suspension type as Automatic
#This df has totally 5 cars 
df_diesel_aut=df[(df['Fuel Type']==' Diesel ') & (df['Suspension']==' Automatic')]
df_diesel_aut
Out[28]:
Year kms Driven Fuel Type Suspension Price Car Model
38 2019 68816 Diesel Automatic 795000.0 Honda Amaze V CVT Diesel
390 2019 50175 Diesel Automatic 2650000.0 Honda CR-V Diesel 2WD
505 2018 50000 Diesel Automatic 730000.0 Honda Amaze S CVT Diesel
761 2018 53955 Diesel Automatic 675000.0 Honda Amaze V CVT Diesel
917 2021 30000 Diesel Automatic 1150000.0 Honda Amaze VX CVT Diesel
In [29]:
#top10 costliest cars with fuel typ as Diesel and suspension type as Manual 
df_diesel_aut=df[(df['Fuel Type']==' Diesel ') & (df['Suspension']==' Manual')]
df_diesel_man_top10=df_diesel_aut.sort_values('Price',ascending=False).head(10)
df_diesel_man_top10
Out[29]:
Year kms Driven Fuel Type Suspension Price Car Model
858 2019 79000 Diesel Manual 1720000.0 Honda Civic VX Diesel
714 2019 23561 Diesel Manual 1100000.0 Honda BR-V i-DTEC V MT
840 2019 40000 Diesel Manual 1090000.0 Honda City i-DTEC VX
929 2019 80000 Diesel Manual 1010000.0 Honda WR-V i-DTEC VX
952 2021 70000 Diesel Manual 1000000.0 Honda WR-V VX Diesel
325 2019 54000 Diesel Manual 990000.0 Honda City i-DTEC VX
738 2017 31000 Diesel Manual 975000.0 Honda City i-DTEC ZX
228 2020 33174 Diesel Manual 920000.0 Honda WR-V i-DTEC VX
941 2019 110000 Diesel Manual 915000.0 Honda WR-V i-DTEC VX
966 2018 60000 Diesel Manual 900000.0 Honda City i-DTEC V
In [53]:
#Graphical representation
fig=px.histogram(df_diesel_man_top10,x='Car Model',nbins=10,color='Price',text_auto=True)
fig.update_layout(width=800, height=500)
fig.show()
In [31]:
#top10 cheapest cars with fuel typ as Diesel and suspension type as Manual 
df_diesel_man_top10_ch=df_diesel_aut.sort_values('Price',ascending=False).tail(10)
df_diesel_man_top10_ch
Out[31]:
Year kms Driven Fuel Type Suspension Price Car Model
492 2013 88000 Diesel Manual 250000.0 Honda Amaze S i-Dtech
585 2014 125690 Diesel Manual 245000.0 Honda Amaze S i-Dtech
806 2014 91332 Diesel Manual 230000.0 Honda Amaze S i-Dtech
921 2013 110000 Diesel Manual 230000.0 Honda Amaze EX i-Dtech
554 2014 66776 Diesel Manual 230000.0 Honda Amaze S i-Dtech
571 2014 205546 Diesel Manual 225000.0 Honda Mobilio V i-DTEC
576 2015 95338 Diesel Manual 225000.0 Honda Amaze S i-Dtech
380 2013 114565 Diesel Manual 212000.0 Honda Amaze S i-Dtech
634 2014 79303 Diesel Manual 200000.0 Honda Amaze EX i-Dtech
229 2013 110000 Diesel Manual 200000.0 Honda Amaze E i-Dtech
In [54]:
#Graphical representation
fig=px.histogram(df_diesel_man_top10_ch,x='Car Model',nbins=10,color='Price',text_auto=True)
fig.update_layout(width=800, height=500)
fig.show()
In [33]:
#Finding correlation among the numerical columns
corr=df.corr()
corr
Out[33]:
Year kms Driven Price
Year 1.000000 -0.366504 0.766076
kms Driven -0.366504 1.000000 -0.309837
Price 0.766076 -0.309837 1.000000
In [34]:
#creating heatmap to have clear identification of the relationship
fig=plt.figure(figsize=(8,6),dpi=100)
sns.heatmap(data=corr,annot=True, cmap='rocket')
Out[34]:
<AxesSubplot:>
In [35]:
#finding:

# Year and price have positive relationship =>year increases, the price is also increasing
#kms Driven and price have negative relationship= KMS increases , the price gets descresing